How to Rank Variables by Group Using dplyr

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You can use the following basic syntax to rank variables by group in dplyr:

df %>% arrange(group_var, numeric_var) %>%
    group_by(group_var) %>% 
    mutate(rank = rank(numeric_var))

The following examples show how to use this syntax in practice with the following data frame:

#create data frame
df <- data.frame(team = c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'),
                 points = c(12, 28, 19, 22, 32, 45, 22, 28, 13, 19),
                 rebounds = c(5, 7, 7, 12, 11, 4, 10, 7, 8, 8))

#view data frame
df

   team points rebounds
1     A     12        5
2     A     28        7
3     A     19        7
4     A     22       12
5     B     32       11
6     B     45        4
7     B     22       10
8     C     28        7
9     C     13        8
10    C     19        8

Example 1: Rank in Ascending Order

The following code shows how to rank the points scored by players in ascending order, grouped by team:

library(dplyr)

#rank points scored, grouped by team
df %>% arrange(team, points) %>%
    group_by(team) %>% 
    mutate(rank = rank(points))

# A tibble: 10 x 4
# Groups:   team [3]
   team  points rebounds  rank
          
 1 A         12        5     1
 2 A         19        7     2
 3 A         22       12     3
 4 A         28        7     4
 5 B         22       10     1
 6 B         32       11     2
 7 B         45        4     3
 8 C         13        8     1
 9 C         19        8     2
10 C         28        7     3

Example 2: Rank in Descending Order

We can also rank the points scored in descending order by group, using a negative sign within the rank() function:

library(dplyr)

#rank points scored in reverse, grouped by team
df %>% arrange(team, points) %>%
    group_by(team) %>% 
    mutate(rank = rank(-points))

# A tibble: 10 x 4
# Groups:   team [3]
   team  points rebounds  rank
          
 1 A         12        5     4
 2 A         19        7     3
 3 A         22       12     2
 4 A         28        7     1
 5 B         22       10     3
 6 B         32       11     2
 7 B         45        4     1
 8 C         13        8     3
 9 C         19        8     2
10 C         28        7     1

How to Handle Ties in Ranking

We can use the ties.method argument to specify how we should handle ties when ranking numerical values.

rank(points, ties.method='average')

You can use one of the following options to specify how to handle ties:

• average: (Default) Assigns each tied element to the average rank (elements ranked in the 3rd and 4th position would both receive a rank of 3.5)
• first: Assigns the first tied element to the lowest rank (elements ranked in the 3rd and 4th positions would receive ranks 3 and 4 respectively)
• min: Assigns every tied element to the lowest rank (elements ranked in the 3rd and 4th position would both receive a rank of 3)
• max: Assigns every tied element to the highest rank (elements ranked in the 3rd and 4th position would both receive a rank of 4)
• random: Assigns every tied element to a random rank (either element tied for the 3rd and 4th position could receive either rank)

The following tutorials explain how to perform other common functions in dplyr: